For a fair comparison, we only replace VTM with other aggregators and leave all the other components in our architecture unchanged. We observe that our proposed method outperforms other aggregators by a large margin. Note that the use of standard transformers for cost aggregation is inapplicable due to high dimensional space of hypercorrelation. Interestingly, although center-pivot 4D convolutions [1]} also focus on locality as swin transformer [2]}, the performance gap indicates that the ability to attentively consider pixel-wise interactions during the self-attention computation is critical. Another interesting point is that linear transformer [3]} and fastformer [4]} that exploit from global receptive fields of transformers achieve similar performance. This confirms that for the cost aggregation, our approach may be more suitable.
